Al-Zaytoonah Private University: statistical indicators, academic achievements and future trends in the light of the annual report for the year 2015 (descriptive analytical study).

This study aims to identify statistical indicators, academic achievements, and future directions of Al Zaytoonah Private University in the light of the annual report for 2015 and the strategic plan for the university (2012-2016). The researcher used the analytical descriptive method. The researcher analyzed the data to identify different statistical indicators And about the university's vision, objectives and values, and the number of students, faculty members and scientific research at the university, indicators of community service, number of foreign students, number of master's students, And the number of administrative staff. The researcher reached the following results: The university works continuously and continuously through the plans and programs that reach the university to a prestigious position locally and regionally in its programs and research activities, and the standards of international accreditation for quality assurance and upgrading. University education, local and regional market support with specialized cadres, qualified expertise capable of development, creativity and active trained leaders. The values ​​of the university are: national integration, continuous learning, fairness in evaluation, sincerity, integrity and leadership. Innovation, and teamwork. The results of the study also found that Zaytoonah Private University of Jordan is one of the leading universities in the field of private higher education in Jordan, and it is one of the oldest universities where it was established in 1993. The university has 7 faculties, 36 academic specialties, 28 undergraduate programs, 8 master's programs, 19 universities, and 25413 graduates by the end of 2015, (70), Of the Master's degree students. The number of students was 7701 students, 210 of whom studied in master's programs and 1477 were non-Jordanians, 19.4% of them were studying in master's programs, The university has 328 faculty members of different academic levels and 555 employees in the administrative departments. The university has seven faculties and includes (28) specialized (8) specializations in the master's degree. Key word: Al Zaytoonah Private University, statistical indicators, academic achievements.

A Comparative Analysis of the Objectives of the National Education in the United States, Jordan, Saudi Arabia, China and Japan

Try researcher in this study to identify the objectives of the National Educational and sources to derive educational objectives and the methods and means to achieve the objectives of National Education and the subjects and courses that are taught in the subject of national education in the levels of education in a range of countries: the United States, Jordan, Saudi Arabia, China, Japan, and use Researcher comparative approach in this study, the researcher depends on the educational goals through books, sources and studies and philosophy of education in these countries, analysis, and the study found all States focused on national education course dramatically and aim to reach the promotion of loyalty and patriotism, loyalty and sacrifice for him as States also seeks to develop national education material to keep up with the explosion of knowledge and technological progress, and focus on the substance of education in its role in addressing the issues and developments and problems facing the community -          Keywords: National Education, the general objectives, citizenship, belonging and loyalt

Monitoring and Classification of Academic Problems Faced by Students in Arab Universities in the Light of Research and Studies: Evaluation Study

This study aimed to analyze and monitor and classification of academic problems faced by students in Arab universities through a review of research and studies in the period between 1985 - 2015, and the researcher used qualitative analytical approach, the study sample consisted of (40) study, and the study results that the most important problems facing students in Arab universities are: Problems courses , faculty, library and course schedule, and the problems of value, guidance, psychological academic, economic, and the existence of a state of fear among some students of university studies, or a state of caution in dealing with others, poor students in stages previous study preparation, weakness scientific level because of the economic situation for a number of students and the different curricula, fatigue and tiredness, rampant bribery in universities, high standard of living, the distinction between students, and problems related to the examinations and the relationship with the faculty and courses of study and area of ​​domestic academic and field body. And the lack of references. Congestion exams in a limited period, the difficulty of contact management and Deans. Keywords: Arab university,  academic problems, students

Identification of Mint Scents Using a QCM Based E-Nose

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner

Signal, Not Poison—Screening Mint Essential Oils for Weed Control Leads to Horsemint

Weed control tries to suppress competitors for a crop and often relies on differential intoxication, making use of differences in uptake, development, or metabolism. We explored the possibility of using natural signals to shift competition in favour of the crop. Using the competitive horsemint (Mentha longifolia) as a paradigm, we showed that essential oils from certain mint species suppress the seedling development of different target species in a specific and efficient manner. The specificity concerned both the donor and the receptor. We demonstrated further that the effect of horsemint oil was specific for actin filaments, and not for microtubules. Since the elimination of actin will impair auxin transport, which is essential for root regeneration in vegetatively propagating weeds, we tested the efficacy of horsemint essential oil in combination with a slow-release carrier against field bindweed (Convolvulus arvensis), a pertinent weed in organic cereal production. We observed that the development of this weed can be specifically blocked, especially if the carrier is worked into the soil. We propose that allelopathic interactions, often relying on manipulative chemical signalling, harbour significant potential for organic weed control

Cellular Base of Mint Allelopathy: Menthone Affects Plant Microtubules

Plants can use volatiles for remote suppression of competitors. Mints produce essential oils, which are known to affect the growth of other plants. We used a comparative approach to identify allelopathic compounds from different Mints (genus Mentha, but also including Cat Mint, Nepeta cataria, and Corean Mint, Agastache rugosa, belonging to sisters clades within the Mentheae) using the standard cress germination assay as readout. To understand the mechanism behind this allelopathic effect, we investigated the response of tobacco BY-2 cell lines, expressing GFP-tagged markers for microtubules and actin filaments to these essential oils. Based on the comparison between bioactivity and chemical components, we identified menthone as prime candidate for the allelopathic effect, and confirmed this bioactivity targeted to microtubules experimentally in both, plant cells (tobaccoBY-2), and seedlings (Arabidopsis thaliana). We could show that menthone disrupted microtubules and induced mortality linked with a rapid permeabilization (less than 15 min) of the plasma membrane. This mortality was elevated in a tubulin marker line, where microtubules are mildly stabilized. Our study paves the way for the development of novel bioherbicides that would be environmentally friendly

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade

Preserving Insulin Secretion in Diabetes by Inhibiting VDAC1 Overexpression and Surface Translocation in beta Cells

Type 2 diabetes (T2D) develops after years of prediabetes during which high glucose (glucotoxicity) impairs insulin secretion. We report that the ATP-conducting mitochondrial outer membrane voltage-dependent anion channel-1 (VDAC1) is upregulated in islets from T2D and non-diabetic organ donors under glucotoxic conditions. This is caused by a glucotoxicity-induced transcriptional program, triggered during years of prediabetes with suboptimal blood glucose control. Metformin counteracts VDAC1 induction. VDAC1 overexpression causes its mistargeting to the plasma membrane of the insulinsecreting beta cells with loss of the crucial metabolic coupling factor ATP. VDAC1 antibodies and inhibitors prevent ATP loss. Through direct inhibition of VDAC1 conductance, metformin, like specific VDAC1 inhibitors and antibodies, restores the impaired generation of ATP and glucose-stimulated insulin secretion in T2D islets. Treatment of db/db mice with VDAC1 inhibitor prevents hyperglycemia, and maintains normal glucose tolerance and physiological regulation of insulin secretion. Thus, beta cell function is preserved by targeting the novel diabetes executer protein VDAC1.Peer reviewe

Phishing websites detection by using optimized stacking ensemble model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent